Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface?
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Brasileira de Ciência do Solo (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832016000100311 |
Resumo: | ABSTRACT It is often difficult for pedologists to “see” topsoils indicating differences in properties such as soil particle size. Satellite images are important for obtaining quick information for large areas. However, mapping extensive areas of bare soil using a single image is difficult since most areas are usually covered by vegetation. Thus, the aim of this study was to develop a strategy to determine bare soil areas by fusing multi-temporal satellite images and classifying them according to soil textures. Three different areas located in two states in Brazil, with a total of 65,000 ha, were evaluated. Landsat images of a specific dry month (September) over five consecutive years were collected, processed, and subjected to atmospheric correction (values in surface reflectance). Non-vegetated areas were discriminated from vegetated ones using the Linear Spectral Mixture Model (LSMM) and Normalized Difference Vegetation Index (NDVI). Thus, we were able to fuse images with only bare soil. Field samples were taken from bare soil pixel areas. Pixels of soils with different textures (soil texture classifications) were used for supervised classification in which all areas with exposed soil were classified. Single images reached an average of 36 % bare soil, where the mapper could only “see” these points. After using the proposed methodology, we reached a maximum of 85 % in bare areas; therefore, a pedologist would have proper conditions for generating a continuous map of spatial variations in soil properties. In addition, we mapped soil textural classes with accuracy up to 86.7 % for clayey soils. Overall accuracy was 63.8 %. The method was tested in an unknown area to validate the accuracy of our classification method. Our strategy allowed us to discriminate and categorize different soil textures in the field with 90 % accuracy using images. This method can assist several professionals in soil science, from pedologists to mappers of soil properties, in soil management activities. |
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Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface?bare soilssatellite imagesspectral sensingmulti-temporal imagesdigital soil mappingsoil remote sensingABSTRACT It is often difficult for pedologists to “see” topsoils indicating differences in properties such as soil particle size. Satellite images are important for obtaining quick information for large areas. However, mapping extensive areas of bare soil using a single image is difficult since most areas are usually covered by vegetation. Thus, the aim of this study was to develop a strategy to determine bare soil areas by fusing multi-temporal satellite images and classifying them according to soil textures. Three different areas located in two states in Brazil, with a total of 65,000 ha, were evaluated. Landsat images of a specific dry month (September) over five consecutive years were collected, processed, and subjected to atmospheric correction (values in surface reflectance). Non-vegetated areas were discriminated from vegetated ones using the Linear Spectral Mixture Model (LSMM) and Normalized Difference Vegetation Index (NDVI). Thus, we were able to fuse images with only bare soil. Field samples were taken from bare soil pixel areas. Pixels of soils with different textures (soil texture classifications) were used for supervised classification in which all areas with exposed soil were classified. Single images reached an average of 36 % bare soil, where the mapper could only “see” these points. After using the proposed methodology, we reached a maximum of 85 % in bare areas; therefore, a pedologist would have proper conditions for generating a continuous map of spatial variations in soil properties. In addition, we mapped soil textural classes with accuracy up to 86.7 % for clayey soils. Overall accuracy was 63.8 %. The method was tested in an unknown area to validate the accuracy of our classification method. Our strategy allowed us to discriminate and categorize different soil textures in the field with 90 % accuracy using images. This method can assist several professionals in soil science, from pedologists to mappers of soil properties, in soil management activities.Sociedade Brasileira de Ciência do Solo2016-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832016000100311Revista Brasileira de Ciência do Solo v.40 2016reponame:Revista Brasileira de Ciência do Solo (Online)instname:Sociedade Brasileira de Ciência do Solo (SBCS)instacron:SBCS10.1590/18069657rbcs20150335info:eu-repo/semantics/openAccessDemattê,José Alexandre MeloAlves,Marcelo RodrigoTerra,Fabricio da SilvaBosquilia,Raoni Wainer DuarteFongaro,Caio TroulaBarros,Pedro Paulo da Silvaeng2016-10-31T00:00:00Zoai:scielo:S0100-06832016000100311Revistahttp://www.scielo.br/scielo.php?script=sci_serial&pid=0100-0683&lng=es&nrm=isohttps://old.scielo.br/oai/scielo-oai.php||sbcs@ufv.br1806-96570100-0683opendoar:2016-10-31T00:00Revista Brasileira de Ciência do Solo (Online) - Sociedade Brasileira de Ciência do Solo (SBCS)false |
dc.title.none.fl_str_mv |
Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface? |
title |
Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface? |
spellingShingle |
Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface? Demattê,José Alexandre Melo bare soils satellite images spectral sensing multi-temporal images digital soil mapping soil remote sensing |
title_short |
Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface? |
title_full |
Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface? |
title_fullStr |
Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface? |
title_full_unstemmed |
Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface? |
title_sort |
Is It Possible to Classify Topsoil Texture Using a Sensor Located 800 km Away from the Surface? |
author |
Demattê,José Alexandre Melo |
author_facet |
Demattê,José Alexandre Melo Alves,Marcelo Rodrigo Terra,Fabricio da Silva Bosquilia,Raoni Wainer Duarte Fongaro,Caio Troula Barros,Pedro Paulo da Silva |
author_role |
author |
author2 |
Alves,Marcelo Rodrigo Terra,Fabricio da Silva Bosquilia,Raoni Wainer Duarte Fongaro,Caio Troula Barros,Pedro Paulo da Silva |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Demattê,José Alexandre Melo Alves,Marcelo Rodrigo Terra,Fabricio da Silva Bosquilia,Raoni Wainer Duarte Fongaro,Caio Troula Barros,Pedro Paulo da Silva |
dc.subject.por.fl_str_mv |
bare soils satellite images spectral sensing multi-temporal images digital soil mapping soil remote sensing |
topic |
bare soils satellite images spectral sensing multi-temporal images digital soil mapping soil remote sensing |
description |
ABSTRACT It is often difficult for pedologists to “see” topsoils indicating differences in properties such as soil particle size. Satellite images are important for obtaining quick information for large areas. However, mapping extensive areas of bare soil using a single image is difficult since most areas are usually covered by vegetation. Thus, the aim of this study was to develop a strategy to determine bare soil areas by fusing multi-temporal satellite images and classifying them according to soil textures. Three different areas located in two states in Brazil, with a total of 65,000 ha, were evaluated. Landsat images of a specific dry month (September) over five consecutive years were collected, processed, and subjected to atmospheric correction (values in surface reflectance). Non-vegetated areas were discriminated from vegetated ones using the Linear Spectral Mixture Model (LSMM) and Normalized Difference Vegetation Index (NDVI). Thus, we were able to fuse images with only bare soil. Field samples were taken from bare soil pixel areas. Pixels of soils with different textures (soil texture classifications) were used for supervised classification in which all areas with exposed soil were classified. Single images reached an average of 36 % bare soil, where the mapper could only “see” these points. After using the proposed methodology, we reached a maximum of 85 % in bare areas; therefore, a pedologist would have proper conditions for generating a continuous map of spatial variations in soil properties. In addition, we mapped soil textural classes with accuracy up to 86.7 % for clayey soils. Overall accuracy was 63.8 %. The method was tested in an unknown area to validate the accuracy of our classification method. Our strategy allowed us to discriminate and categorize different soil textures in the field with 90 % accuracy using images. This method can assist several professionals in soil science, from pedologists to mappers of soil properties, in soil management activities. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832016000100311 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832016000100311 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/18069657rbcs20150335 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência do Solo |
publisher.none.fl_str_mv |
Sociedade Brasileira de Ciência do Solo |
dc.source.none.fl_str_mv |
Revista Brasileira de Ciência do Solo v.40 2016 reponame:Revista Brasileira de Ciência do Solo (Online) instname:Sociedade Brasileira de Ciência do Solo (SBCS) instacron:SBCS |
instname_str |
Sociedade Brasileira de Ciência do Solo (SBCS) |
instacron_str |
SBCS |
institution |
SBCS |
reponame_str |
Revista Brasileira de Ciência do Solo (Online) |
collection |
Revista Brasileira de Ciência do Solo (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Ciência do Solo (Online) - Sociedade Brasileira de Ciência do Solo (SBCS) |
repository.mail.fl_str_mv |
||sbcs@ufv.br |
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1752126520802934784 |